ArtisanCAD: An Industrial-Level CAD Agent with Expert-Grounded Knowledge Distillation
TLDR
ArtisanCAD is an industrial CAD agent using expert knowledge distillation and CAD-IR to generate production-ready B-Rep models from text.
评分理由
The paper introduces a novel CAD intermediate representation (CAD-IR) and expert knowledge distillation to address ambiguity in text-to-CAD. Strengths include a focus on industrial-grade parametric modeling and B-Rep execution, but the abstract lacks complete benchmark results and details on limitations.
Read-first 评分解释
综合优先阅读分 58，由主题、引用、图谱、方法、可复现性和近期性等信号加权得到。 原始总分保留为 87。
研究版图角色
排序敏感性
稳定性：volatile；排名波动范围：15。
关键词评分
深度分析
创新点
- CAD intermediate representation (CAD-IR): an executable procedural representation encoding parameters, ordered operations, MCP tool bindings, dependencies, generated entities, and verification rules
- Skill-guided industrial CAD agent with expert-grounded knowledge distillation, distilling expert CATIA procedures into reusable parameterized skills
- Retrieval of expert-derived skills, CAD-IR instantiation and revision, execution through a dedicated CATIA-MCP backend, and multi-view visual feedback for iterative refinement
- Bridging ambiguous or intermediate-level textual prompts to full executable CAD operations via CAD-IR scaffold
方法
ArtisanCAD uses a CAD intermediate representation (CAD-IR) to distill expert CAD procedures into reusable skills and to scaffold vague prompts into executable operations. It retrieves expert-derived skills, instantiates and revises CAD-IR, executes through a CATIA-MCP backend, and applies multi-view visual feedback for iterative refinement, generating production-ready B-Rep models.
关键结果
On the Text2CAD benchmark, CAD-IR reduces mean Chamfer Distance from 14.83 to 9.88 for intermediate prompts. On four complex automotive components, expert CATIA recordings distilled into skills enable generation of editable CATIA-native B-Rep models for new variant requests.